The Top 3 Ways AI Is Reducing Onboarding Time for Sales Reps

Employing new sales reps usually involves an onboarding process. But the typical onboarding process is getting longer and less efficient as time goes on. While there are many ways to improve onboarding for sales reps, organizations can get the most “bang for their buck” by incorporating knowledge automation into their onboarding program through the use of AI.

Proof That Sales Onboarding is Stuck

An eye-opening report from Qvidian in 2015 surveyed hundreds of executives and sales leaders from various industries, markets, and company sizes to assess their objectives and challenges. What they found was that sales organizations were struggling with getting the right information into their new sales reps’ hands, and doing so in a way that led to success. Noted in the report:

  • 36% of organizations mentioned “ramping up new sales reps takes too long” as one of the top reasons why their teams were failing to make quotas.
  • Indeed, onboarding of sales reps took an average of 7 to 9 months (measured as the time until a sales rep becomes fully productive). (Note this is an average: 1 in 5 sales reps take over a year to reach this level of performance!)
  • Sales teams were not meeting quotas largely because they were failing to personalize the buyer journey for their customers and effectively communicate value.
  • 55% of the organizations surveyed indicated that part of the reason they failed to communicate value was that reps were struggling to identify the tailored selling content among all the materials they had.

This inefficiency carries a steep price tag, to say the least. Not only does slow and inefficient onboarding mean lost productivity, but it also leads to higher turnover, added expenses for recruiting and training, lower employee morale, and a net negative impact on clients. By one estimate, it costs approximately $115,000 to replace a sales rep…and about 28% of sales reps, or more than a quarter, turn over in a given year.

Indeed, Zappos CEO Tony Hsieh is well known for completely restructuring his company upon learning that bad hires were costing him well over $100 million a year.

Why Sales Onboarding is Failing

That sales onboarding is failing across industries is not a secret. Why it is failing, though, is something that makes sales managers scratch their heads.

That’s because the issues do not stem from the onboarding program itself. We have identified four main reasons why traditional onboarding is failing in today’s sales environment, and they all have to do with the flow of information:

Reason #1: Distribution. Sales reps are scattered across the country, and even across the globe. They are often not located in the same places as the subject matter experts and marketing teams that they need to engage with for information.

Reason #2: Product Evolution. Products are evolving faster and faster. While the innovation is great, what was true of a product line a few months ago might not be true today. Sales reps cannot rely anymore on a single “information dump” at the start of their tenure.

Reason #3: Customer Expectations. Customers and prospects expect faster responses in the digital age. The amount of onboarding needed to keep information fresh in a sales rep’s brain is mind-boggling.

Reason #4: Increased Competitive Pressures. Yes, products and expectations change. But so does the competition. Knowing the competitive landscape and the target market takes much research and study. When sales reps are given this task, they are expected to do more, in less time. When it is given over to marketing, the information too often remains “siloed.”

The Top 3 Ways AI Can Reduce Onboarding Time

Sales is a uniquely human endeavor. How can AI help sales reps become more productive more quickly? We see three main ways that AI can reduce onboarding time:

1. By eliminating the need for “perfect” product information.

If product information is always changing, perhaps the best approach is to stop trying to frontload that information into the onboarding period. By using a knowledge automation tool like Nimeyo, organizations can ensure that sales reps have the right information at the right time, without the need for extensive search or periodic training.

2. By finding tailored content for sales reps in a timely fashion.

Remember, sales teams are, by and large, failing to communicate value because reps struggle with identifying tailored selling content within their own organizations. Wading through large amounts of content is something AI is now really good at doing. Sales reps can use knowledge automation tools to identify specific pieces of content that will help enable sales teams, even down to the level of the individual customer. This cuts down on the time that sales reps need to become familiar with sales materials and helps facilitate their researching the market.

3. By gaining insight into success and failures in a timely manner.

Many organizations struggle when it comes to gaining visibility into the sales cycles. And when they do not have visibility, it can lead to bad decision-making—or no decision-making at all. This is especially true when employees who are not a good fit are allowed to linger in an endless onboarding process.On the other hand, proper sales analytics can identify patterns, allowing organizations to duplicate successes and minimize failures. This can feed back directly into coaching efforts.

And, with less time spent on product information updates and search, more time can be spent on that kind of coaching during those first formative months.

So will AI fix everything that is wrong with sales onboarding today? Of course not. Crucial elements like mentorship and processes that follow best practices will still be needed. But knowledge automation systems can fix the element that is causing the onboarding process to balloon in modern organizations: The sheer amount of knowledge needed to be successful. Cut this bloat from the onboarding process, and your sales reps will become productive much more quickly.

Why Traditional Knowledge Management Doesn’t Work in the Digital Age

Knowledge management—the efficient handling of information and resources within a commercial organization—is not a new concept in business. In fact, it has been around since the ’90s. But even as businesses are still trying to get a handle on knowledge management practices, the concepts and processes developed back then are aging to the point of becoming obsolete.

In other words, knowledge management as we know it is not destined to survive the digital age. The issue is that digital technology has, far from making management easier, contributed to the explosion of available information.

To survive the digital age, organizations will need to start thinking, instead, in terms of knowledge automation.

Proof that Knowledge Management Falters in the Digital Age

Knowledge management grew out of a realization that large organizations needed to organize their information in a more holistic manner. This meant capturing and retrieving needed information from a variety of sources: databases, documents, policies and procedures, and even the expertise and experience implicit in the practices of individual employees.

This sounds like just the sort of thing that should be easy with cloud technology and better integration tools. But consider:

Information is everywhere in organizations. But employees are spending an inordinate amount of time trying to find just the information they need, when they need it. As the information in an organization grows, this situation gets worse, and management of that information itself because an ever greater task.

In other words, knowledge management has led to better collection of, and access to, information…but it has failed to make the retrieval of pertinent knowledge faster or easier.

What Knowledge Management Promised

Still, the original needs that knowledge management was supposed to address will not go away. In an ideal world, proper knowledge management would enable things like:

  • Rapid data-driven decision-making
  • Fast dissemination of relevant information across siloes
  • Minimization of duplicate efforts
  • Broadcasting best practices and solutions in a timely manner
  • Better utilization of existing knowledge assets, both formal and informal
  • Better leveraging of SMEs’ knowledge
  • Better use of SMEs’ time
  • Standard and repeatable processes, procedures, techniques, and templates
  • More accurate and timely information for sales teams
  • More rapid response by customer service and support teams

Notice that these benefits are about speed and relevance as much as anything. But these are exactly the areas where traditional knowledge management has been slow to develop.

Even more disheartening is the cost burden that knowledge management has brought, without realizing return on those investments. Improper planning, design, support, and evaluation can easily lead to a lack of widespread contribution, which further erodes usefulness, relevance, and quality. And, even when moderately successful, using older knowledge management system is costly to maintain and difficult to scale because of their dependence on “top down” knowledge management rather than “self-building” knowledge.

From Knowledge Management to Automated Knowledge Curation

“Knowledge automation” is becoming a popular way to describe how machine learning and artificial intelligence (AI) can be used to automate more of the knowledge management process. (“Automated knowledge curation” is another, although less popular. It means much the same thing.)

Much of the knowledge within organizations is generated by the activities of your people. They email questions and answers back and forth. They use informal communication tools like Slack. They produce wikis and sales sheets and blog content. All of this knowledge is there in the organization—it just needs to be curated and made automatically available with the touch of a button.

This is the idea behind “self-building” knowledge: knowledge that is already present in an organization and that is continuously curated instead of “managed.” Knowledge automation creates access to self-building knowledge, rather than relying on “top-down” management.

After all, information itself is cheap; as the list above shows, it exists in many ways, and in many different forms. That information only becomes knowledge when the right slice of information can be applied in the right situation.

For example, suppose a prospect has a question about a particular feature on one of your newer products. Your sales team should be able to answer that question without wading through a pile of sales sheets and development wikis—or worse, waiting for an answer from a SME halfway around the world.

Another example: A customer has an issue and reaches out to your organization via social media. Your customer service team now has to query several separate systems in order to handle this: a case management system, an internal incident management system, a knowledge base, and several off-band communication channels. This would usually take a full day; imagine, though, if the relevant knowledge in these systems could be made available instantly, so that a reply could be made within the hour. (In fact. call center costs and volumes can decrease by as much as 30% when better search and automation tools are implemented.)

These are just a couple of simple examples—you can follow the links for more detailed use cases. Still, they are good examples of why traditional knowledge management is not surviving the digital age. They also show why we developed our app, Nimeyo, as a way to automate the gathering of information across channels. It is a knowledge automation solution that brings both speed and relevance to those who need the right information at just the right time.

Like Beanie Babies and dial-up internet, some things should be left in the ’90s. It’s time to update the way we access information in this digital age.

Two AI Use Cases for Customer Support and Services

In our last post, we highlighted the fact that many companies assume that the more “human” parts of business —sales and customer service—have little to gain from Artificial Intelligence. Of course, this assumption is incorrect, and liable to mislead companies who could otherwise stand to benefit.

Consider:

  • According to Forrester, 72% of businesses say that improving the customer experience is their top priority.
  • Most contact with customer service now takes place via the web using a chatbot, via email, or via social media. The set of skills and tools needed here are different than, say, handling a case via phone.
  • Customers have ever-growing expectations with regards to response time. A decade ago, customers were willing to wait 24 hours for an answer to a question or a solution to their problems. Now they want an answer right away…if not instantly.
  • As business grow and expand their global reach, more and more customer support cases begin to look similar. Solving each case independently is burdensome, if not impossible.
  • The average customer triage and resolution cycle takes five or more steps having to do solely with information search among the organizations various data sources.

In other words, the need for a human being with “people skills” is diminishing just as the strengths of artificial intelligence agents—such as the ability to query multiple data sources quickly and efficiently—are coming in high demand. Indeed, one prediction holds that, by the year 2020, more than 85% of all customer interactions will be handled without the need for a human agent.

But what does customer support via artificial intelligence agent look like? Again, we can illustrate this best with two use cases around our own knowledge automation solution, Nimeyo.

Use Case 1: Resolving Customer Issues When Knowledge is Siloed

The Context:

Today, customer service reps are expected to resolve customer issues faster and faster, even as they take on huge case volume to justify their job roles. In order to do a good job of meeting customer expectations and succeed in their roles, a single pane of information and knowledge access is essential.

The Challenge:

Again, the typical customer resolution in an organization of any appreciable sizes takes researching five or more data-sources. These include:

  • Querying customer-facing case management systems (such as Salesforce Service Cloud or Zendesk) to identify duplicates and bring up relevant contextual information
  • Comparing across internal incident management systems (such as Jira) to find similar cases already being addressed, or that have recently been addressed successfully.
  • Searching KnowledgeBase articles and wikis for quick resolution of common problems, or concise answers to frequently asked questions
  • Combing through off-band but relevant conversations in emails or Slack channels

Already, this process is pretty daunting. When you consider that two or more of these steps could be taken for cases that are very similar, and for which solutions already exist, it becomes painfully obvious how much time is wasted and productivity sacrificed. Currently, organizations are struggling to find ways to integrate these various sources into a single pane.

How AI Helps:

This scenario is easily fixed with a solution like Nimeyo knowledge automation. Using Nimeyo, customer service reps can address cases more readily, thanks to instant access to knowledge of similar cases across content silos of customer issues and internal product ticket systems.

Nimeyo can also integrate with management systems like Salesforce, as well as incident management systems like Jira and chat channels like Slack. It can then access these systems instantly and use the information in them to help zero-in on the resolution for a given case, relieving the customer service rep from having to do these searches manually.

More importantly, Nimeyo helps customer service reps deflect more cases by giving them increased visibility of similar cases across customer issues and internal product ticket systems.

All of this results in more rapid response which, ideally, leads to improving their first contact and/or first time resolution times.

Use Case 2: Self-help Bots For Customers and Service Teams

The Context:

As we all know, a lot hinges on having a positive customer service experience: It can mean the difference between a loyal customer, and a disgruntled one. Speed and accuracy matter crucially, and customer demand instant responses. If they don’t find an answer immediately, they are disappointed and are quick to share their bad experience on social media or other public channels.

But increasing complexity of products and services, along with the high turnover rate of most call centers, means that it is almost impossible for service reps to keep up with the content needed to resolve issues in a timely fashion.

These dynamics are fundamentally changing how both customers and service reps seek out information. For example, the majority of Millennials actively avoid situations for which human interaction is necessary to solve an issue, much preferring self-service options instead. One study of the generational divide in customer service found that a whopping 72% of Millennials believe a phone call is not the best way to resolve their customer service issues.

So how are consumer resolving their issues, if not calling customer support? Right now, they are using a mix of chat bots on websites, social media sites for the relevant brands, chat channels, and Google searches. In other words, they are already going with digital self-help solutions.

The Challenge:

Companies face two choices: Either improve the self-help bots they make available, or better empower their service teams to compete with these bots.

Most of the current self-help systems are web centric, so customers are relegated to searching for a solution themselves—and are often confronted with more pages than they are willing to review. Even if they do find the  answer they seek, it may not be the most accurate or latest answer.

That said, many Customers are still “put at ease” knowing that there is a customer service rep in the interaction; but this “human touch” engagement is costly, often only available during business hours, and is (for the most part) unscalable.

How AI Helps:

With a Nimeyo AI solution, customer service organizations can create a foundation of knowledge and insights from approved content sources like FAQ databases, product documentation or issue tracking systems. Subsequently, bots or auto responses act as the first line of defense to respond to common question with known answers or fixes.

When a customer sends an email to a support email address, the email autoresponder can look at the knowledge available to instantly respond with links to most relevant answer. If the customer is happy with the answer, then the customer service team can mark the issue as resolved. If the customer indicates that more assistance is needed, a rep can reach out for additional information.

What about availability and scale? Typically, a customer service chat is available only during business hours (unless you have a globally distributed service teams.) However, if a customer initiates a chat with a rep during off hours, an auto responder bot can respond to customer query with knowledge from approved sources. Queries such as the status of a case, answers to FAQs, or product specific questions can be responded to in seconds without any human intervention. Again, the chatbot can be the first line of defense before a rep needs to be engaged.

Again, these are just two simple ways that AI agents are changing the face of customer service. Counter to many of the assumptions surrounding AI, human beings will always have a role to play in customer support, since there will always be difficult cases requires a person’s  ability to understand the nuances of the case and find creative solutions. Increased productivity comes when human beings can be freed from routine and easily-solved cases, and allowed to focus on more complex cases and tasks. Artificial Intelligence can potentially leave service reps free to tap into the critical thinking and problem-solving skills, not to mention emotional intelligence, when they are needed most.

If it still sounds like a pipe-dream to empower human interactions through AI technology, we recommend you try Nimeyo yourself to see how this can be done in your organization. Sign up for a free demo, and we would be delighted to give you a tour and show how the Nimeyo AI can be best used by your customer service teams.

Two Real-Life AI Use Cases for Sales Productivity

That Artificial Intelligence (AI) is having a huge impact on business is nothing new. Market projections of revenue gained from AI deployment will be in the neighborhood of $36.8 billion by the year 2025, and are already at the $643.7 million mark today.

But for all the buzz surrounding AI and its cousins, Big Data and digital transformation, it has been hard to imagine what concrete business applications the technology would have outside of business intelligence and marketing and CRM. Indeed, one assumption we keep running into is that the more “human” side of business—sales and customer service—has little to gain from AI.

That assumption is being proved wrong. For one thing, sales roles are getting closer scrutiny, with over 3 million sales jobs expected to disappear over the next 5 years. This means that fewer and fewer reps will be expected to drive more and more growth, just to survive. It also means a keener focus on activities that lead directly to more closes. In short, higher sales productivity.

 

And yet, one of the biggest obstacles to greater sales productivity is knowledge hunting, or more precisely, the time and effort sales reps have to put in uncovering just the right bits of information they need from the content silos at a given time.

This is something AI has become incredibly good at: Extracting just the needed information from a large base of unstructured data and conversations. We can illustrate this best by looking at a couple of the use cases for our own AI knowledge automation solution, Nimeyo. Nimeyo provides some good examples of how sales teams are now using AI as their sales enablement tool of choice.

AI Use Case #1: Smart Knowledge Bases and Email Autoresponders

The Context:

Sales teams depend critically on being able to answer a prospect’s questions quickly and accurately. For a large sales organization, this requires a huge chunk of a sales rep’s time. Complicating this picture is the fact that many organizations are geographically scattered, and large numbers of reps are expected to learn about complex products and respond to customers’ queries about them in a short amount of time.

Reps often rely on sales enablement and product/technical marketing to provide them with technical information about product features, capabilities, solutions, and roadmaps. Many have set up email distribution lists to align sales and marketing teams in an effort to control the flow of customer-related information.

The Challenge:

What these distribution lists look like in real life can be less than stunning. Reps ask questions and then wait for responses from a subject matter expert (SME)—often someone located halfway around the world. This means an average response time measured in days, with many inquiries requiring multiple follow-ups. Even after such an extended process, roughly 50% of information requests are left unanswered.

Life is not easy for the SMEs either, who often find that more than a third of the questions they receive are repetitive, having been answered in previous conversations. While SMEs are using wikis and other tools to disseminate knowledge, these rarely are used regularly by reps. Nor do SMEs have a way to “check the work” being done by sales reps by providing feedback on things such as product decks. The result is a lot of redundant effort, and less time for sales teams to actually work on closing deals.

How AI Helps:

Nimeyo’s AI solution allows these kinds of sales organizations to automatically build and dynamically sustain a knowledge base of information from email threads in a distribution list. An email autoresponder can be set up that automatically reviews each new email sent on various distribution lists, automatically identifies the information request, and responds with the most appropriate knowledge. The responder can also carbon-copy the relevant SME so he or she can further contribute or revise the answer, if needed.

If a response is not available (because the product being discussed is brand new, for example), the AI system waits for responses to the email thread from the SMEs, and that knowledge is then added to the knowledge base for future use.

With this AI technology, reps are able to get their questions answered in under 30 seconds—from anyplace, and on any device, using just their favorite email client. They no longer have to monitor emails on distribution lists but can still leverage the collective knowledge generated by the people on that list over time.

For their part, the SMEs no longer have to deal with repetitive questions, and when they do respond to questions, they can be assured that their contribution will be leveraged in all future inquiries. Large organizations that rely on email as a primary tool for collaboration can leverage a tremendous amount of tribal knowledge that field teams generate for efficiency and effectiveness.

AI Use Case #2: Extracting Information from Unstructured Collaboration Tools (Slack)

The Context:

Today’s modern sales organizations are using popular collaboration tools like Slack, HipChat or Microsoft Teams to communicate and collaborate. In some of the growing organizations, channels in Slack or HipChat are extensively used to discuss products, pricing, competitive situations, solutions, and related topics. These channels are treasure troves of tribal information relating to product, customer, and business.

The Challenge:

Slack, to take one example, started out as a medium for informal communication. It quickly became an alternative to email in many organizations, with broad adoption. However, tools like Slack don’t impose any logical structure on the content thus generated, focusing solely on making conversations flow naturally. As a result, search and retrieval are slow and painful.

While there are benefits to this kind of communication, more and more sales organizations are now realizing the costs of using such synchronous, always-on, noisy mediums. Sales reps in particular are spending more and more of their time trying to extract the useful information they need from a sea of noise.

How AI Helps:

Nimeyo’s NLP-driven bot can provide answers to sales-related questions right within Slack.

Sales reps can direct-message the Nimeyo bot in Slack and get relevant information, irrespective of where that information may reside.

For example, the Nimeyo bot can fetch a competitive battlecard from Google Drive or Box, or fetch the status of a particular deal from Salesforce CRM or status of a customer issues from Zendesk or answer an RFP question from RFP database. The bot can also present snippets of conversations that might have occurred within Slack itself and that are relevant to the request.

The Nimeyo bot also becomes a virtual user in the channel and listens to the chatter. When a real question is asked by a rep for which it feels it has an answer or relevant information, it will jump in with that information—like a real human being. Again, the information source can be any document, CRM, ticketing system, email, or the Slack channels themselves.

 

In short, AI can be used to sort through unstructured data, extract relevant knowledge, and present that knowledge on demand as if it were a regular user—but without the delay.

 

These are just two simple ways that AI agents are automating knowledge-hunting activities. Sales organizations that do this remove a major time-waster, thus freeing their reps (and their SMEs) to focus more on revenue-generating activities and boosting productivity. In fact, Gartner predicts that, by 2020, 30 percent of all B2B companies will employ AI to augment at least one of their primary sales processes.

 

And if you are still in doubt… we would love the chance to prove the value of such automation. Sign up for a free trial of Nimeyo, and we would be delighted to give you a tour and show how the Nimeyo AI can be best used for sales productivity in your organization.

Bottom-up Sales Enablement

salesenablement

Sales Enablement is an essential element for any company trying to increase their sales productivity.  Sales enablement goes by many definitions and has many tools, but in essence, it is the creation and distribution of sales collateral to help salespeople move prospective customers faster through the sales pipeline.

Traditionally, the creation of sales enablement content is centralized, meaning that a few select individuals – either in Sales Enablement or in Marketing – control the flow and content. Although this approach, in theory, ensures the most accurate and relevant information is distributed to sales reps, in reality it can be problematic for a couple of reasons. First, companies generally have to allocate resources, including dedicated headcount, to coalesce and oversee any and all changes. Not only is this costly, but it means that the people who are creating content for sales enablement are separated from the front-line realities of the sales process.

Second, in order for the sales enablement efforts to remain effective, content must be constantly updated and changed. Product requirements, roadmaps, priorities, customer feedback, customer perception, and customer needs all change in real-time.  By the time it takes for information to bubble up from the boots on the ground to the top, and then back down again – your organization is already behind.

While top down sales enablement is important, it can benefit immensely when combined with bottom-up sales enablement. Bottom-up sales enablement places trust in the sales force’s ability to self-regulate and internally communicate. Bottom-up sales enablement works by capitalizing on the natural and abundant information exchanged amongst sales reps themselves and between sales and marketing. These natural conversations through emails, chats, document exchange, etc. are tremendously context and content rich, and if leveraged correctly, allows the sales force to be tremendously agile.

There are tools in the market that almost everyone uses. For example, Slack/HipChat for messaging and email/Box/Google Drive/SharePoint for product document sharing are standard in growing and mature sales organizations.  Unfortunately, this tribal, customer-centric, valuable information is never leveraged to improve sales performance. A true bottom-up sales enablement tool should allow sales rep to quickly and effortlessly gather needed information by leveraging what was already discussed or documented from prior similar situations.

For example, if a sales rep extensively uses email as her preferred means of communication, a tool that would not only allow her to share information to her colleagues, but also allow her to consume information within email would bring tremendous efficiency. Similarly for groups that have adopted Slack for casual conversations, providing the ability to share and consume sales intelligence from within Slack would be very effective.  After all, sales reps are judged on selling, not on how well they navigate internal information silos. This truly bottom-up approach where information flows up from reps and becomes knowledge that then flows down to the people who really need it in real-time is the holy grail of sales efficiency.

We at Nimeyo have spent years developing a system that aggregates information from scattered sources and builds a layer of intelligence using classification, personalization and learning. Moreover, this self-built intelligence is then delivered back to reps when and where they need it through tools they already use, like Salesforce, Slack, email, or mobile.  If your sales reps are spending more time looking for information than actual selling, drop us a note and we will show you how we can help.

Lessons and Challenges from Slack

blog_img_03

We at Nimeyo have always been fascinated with how employees communicate, and an intriguing company on the forefront of this is Slack.  Slack has succeeded where many others have failed.  Heck, Microsoft almost bought it for $8 billion. Given that, what has it done right that we can all learn from and what challenges does adopting Slack create?

How did it get traction?

On the surface, Slack does not look all that appealing.  It is, after all, just IM plus channels.  But that’s the key to its success.  It is not reinventing the wheel nor overwhelming the user.   Slack presents something we all know and makes it really easy to use.  Just a browser and you are ready to go.

Simplicity is the key, and Slack nailed it.  Slack just fits into an employee’s natural workflow.

Making communication conversational

One way Slack fits right into the employee’s workflow is by facilitating conversation.  Sure, there are documents, e-mails, wikis – but information often flows through conversations.

And what is a conversation?  A series of quick back and forths – something humans are naturally good at.  And Slack allows this in an electronic forum.

When it comes to this type of communication, there is always a tug back and forth between structured, formalized knowledge, and informal conversational knowledge.  And Slack has succeeded by knowing that employees generally prefer the latter.   In other words, people would rather have ease and simplicity– they don’t want to be bothered with formatting, manipulating, or overall shaping the data to something that is useful later.

Doesn’t make the user change behavior

Slack also allows integration with many third party tools and services:  e-mail, Box, Google Drive, Dropbox, etc.  Employees can still use their favorite means of electronic communication and have that be automatically integrated into Slack.  If an employee still prefers e-mail, she can use e-mail.  If she wants to put information into a document on Dropbox – no need to duplicate that information into Slack. In other words, Slack integrates, but doesn’t force change.  After all, everyone is a creature of habit in one way or another.

But is Slack taking away from organization?

But the picture is not all rosy.  Conversations do have weaknesses – they are tough to organize.

With Slack, there’s less need for documentation, formalized transfers of information, seminars, etc.  While this saves time, it also means that the expertise will often remain siloed and only be passed on, piece by piece, when needed due to the chatty nature of communication.  But in the age of rapid turnover, what happens if an expert leaves?  Does all their knowledge leave with them?

And is adding yet another channel of communication worth it?

It’s important to remember that Slack is another tool, and tool fatigue sets in.   With real estate space on the screen becoming even more precious and people being pulled in many different directions, very few employees clamor for another tool to deal with.

What can be done about these challenges?

While Slack is a great tool, we at Nimeyo understand the challenges that it presents

  •  Keeps information siloed by detracting from more formalized training methods
  •  Information is haphazardly placed, with little organization, and plain tough to find
  •  Information is in yet another place, making discovery much more of a chore
  •  Yet another tool amongst a myriad of tools that the average employee has to deal with

There are many ways to go mitigate this.  Slack provides APIs to integrate channels into whatever management tool you wish.  They also have Apps which provide integration with 3rd party tools like Box. Unfortunately, this requires a lot of IT resources from employees who would rather be working on core services.

qPod by Nimeyo provides a ready-built solution by processing Slack information into a searchable, interactive, question and answer system.  Information flow can be congregated from various sources like Slack, e-mail, Box, SharePoint, Salesforce, JIRA, Confluence, and much more.  And the knowledge gained can be easily accessed to help find the information you’re looking for in bite sized pieces.  Information from Slack is more structured, available in the user’s workflow, and can easily be discovered.  In other words, qPod takes Slack to a higher level.

qPod and Slack together!

qPod and Slack work well together with our newly introduced “qPod by Nimeyo” Slack App.

Slack integration within qPod allows users to simply login via Slack – no need to setup a separate login username and password.

And once integrated, channel messages can be imported and accessible throughout qPod.

channelsales

And with our “qPod by Nimeyo” Slack App, accessing qPod and posting information is a breeze with easy to use Slack Commands.

qpodslack

If you’d like to take a test drive, please head over to our Slack App page and sign up.

Initial Thoughts on Google Springboard Announcement

google-springboard

Google just announced something really interesting. They’re building a completely new search product for enterprises called Springboard. Now, in order to understand what Springboard is and the rationale behind it, we have to go back a couple years.

In 2008, Google came out with something called the Google Search Appliance, or GSA for short. The idea was to bring Google’s search prowess to corporations. If you’re not a big business, however, chances are that you haven’t heard of it, and that’s fine. The GSA was an on-premises hardware search appliance. In other words, it was an appliance that allowed employees to search for information that was scattered across all their business systems. This is definitely a ripe opportunity because studies, including a report from McKinsey, have shown that workers spend 20% of their time looking for information. But the GSA, like similar offerings from other enterprise search vendors like HP/Autonomy, struggled to get a strong foothold in enterprises. First, in order to use the technology, you had to deploy a hardware appliance and spend endless hours configuring and tuning for quality results. This made it more difficult for small business to benefit quickly. But more importantly, the user interface and underlying technology failed to foster adoption and engagement.

To explain this problem with a very simple example, let’s say you’re on your way to work and wondering “why does my dog keep eating my shoes”. You are so curious that you decide to search for the answer on Google. One of the things Google does when you search is it finds documents that use those words with the highest frequency. With That data, along with information on how many other people found that document useful, Google ranks the results for you. The top result would be the most relevant based on word frequency, context and social proof of quality. And this works really well for consumers.

As you can imagine, this model quickly becomes problematic in business. First, information sources are more diverse and systems often don’t interoperate. Second, social proof is almost non-existent given that not many people search for similar stuff to begin with. And finally, enterprise information needs are very specific and demands accurate and time-sensitive results. For example, when you search for “product roadmap”, the search engine would give back any document with the word “product” and “roadmap” in it. It could be a roadmap published 5 years ago. It could be an old email sent to a colleague. It could be a Salesforce entry of an old customer. For a more in-depth explanation of this topic, refer to Can Enterprise Search Effectively Serve Employees’ Needs?

Enter the Google Springboard. Announced on June 13, 2016, Google Springboard is, in many ways, the GSA 2.0. But it is expected to be much more. First, it will be cloud-based, meaning that you won’t have to worry about installing a piece of hardware into your data center. But more importantly, you will see a much more seamless experience in terms of manageability. This should open it up to small and mid-sized businesses. Secondly, in the long run,the search experience will be more predictive than reactive. This is the holy grail of enterprise search as it gives customers access to information without having to change their workflow. Springboard brings tremendous credibility to this space. Finally, Springboard will have an improved mobile interface for the employees that are out and about, possibly of  bringing “Google Now” cards to enterprise data.

One of the big limitations of Springboard, at least initially, is that it only searches across Google apps like Gmail, Google Drive, and Calendar and not third party apps like Salesforce and Zendesk. This effectively ties you to Google’s own ecosystem. We hope Google will enable searchability of other data-sources through a rich partner ecosystem. For now, we can only wait and see what this tech giant, founded by creating a large scale, consumer facing search engine, can produce for a smaller scale, enterprise environment.

Wrangling Enterprise Data

blog_img_02

In our previous blog, The Myth of One Golden Informational Warehouse, we described the ideal informational warehouse where all relevant corporate information resides in an easily searchable, coherent, and up-to-date form. We also discussed how individual preferences, habits and organizational culture make it hard to achieve such a warehouse.

In this article, we will try to analyze this problem from the “systems” perspective. Systems, in this case, consist of tools, processes and the data that they hold and operate.

Data and Tools

blog_enterprisedata

The above diagram describes various types of data and the tools and services a typical organization uses. For the sake of simplicity, this diagram only includes systems that are pervasive and used frequently by employees.

As you can see, the tools on the left tend to be “systems of engagement” – natural, conversational and dynamic; while tools on the right tend to be “systems of record” – highly structured, curated and managed. Moreover, communication mediums like E-mail and IM are noisier (e.g. due to language ambiguities) from the information perspective compared to structurally curated information repositories like CRM.

Unfortunately, internal knowledge organizations in enterprises have this unenviable task of wrangling all these sources of information into a cohesive, searchable, navigable solution that is non-disruptive, secure, and easy to use.

The Problem/Solution Gap

It is quite easy to see why knowledge or IT organizations prefer stricter, more structured services (right side of the spectrum). For example, extracting a customer name or an employee assigned to a task is much easier from a CRM or PM tool where specific fields capture that information. However, it is much harder to extract such information from Slack messages or E-mail conversations.

On the other hand, employees hate to curate information for the system. They have a job to do after all, be it development, sales or marketing. Documenting something is just an overhead they would rather avoid.

And therein lies the dilemma. Employees are communicating through IM or E-mail and generating tons of useful information in a dynamic work environment – and almost none of it makes it to the system of record. Meanwhile, the more structured sources that contain no IM or E-mail are the places from where business intelligence is derived.

Needless to say, this deep rooted and pervasive disconnect has created a gap in the way employees access corporate information relevant to their jobs.

Enterprise Wiki: Shifting to the Right

A good example of the tension described above is Enterprise Wiki. A few years ago when wikis were all the rage, there was a massive effort to shift collaboration from chat and E-mail to wiki.

blog_wiki

As demonstrated in the diagram, many forward looking organizations thought that this new initiative would make their data easier to manage by shifting to a more structured, organized, and system ready service.

Unfortunately, this shift required a change in behavior from employees as they had to learn a new user interface and process which was less natural than before. While some appreciated the change, others were not quite so enthusiastic. For example, Product Marketing may love wiki pages as they are well organized and easy to manage. However, for the Sales team (supposedly the benefactor of this content), such a solution would be a burden as they may not have access to corporate network all the time and may want a mobile friendly solution. They just need answers, not documents. They would naturally stick with E-mail or chat.

Indeed, research from MITRE has suggested that people have resisted putting information onto wikis because:

First, we uncovered a reluctance to share specific information due to a perceived extra cost, the nature of the information, the desire to share only “finished” content, and sensitivities to the openness of the sharing environment. Second, we discovered a heavy reliance on other, non-wiki tools based on a variety of factors including work practice, lack of guidelines, and cultural sensitivities.

In other words, the failed adoption of wikis was part of the systematic difference in expectations between the consumer and the producer of the information.

The Ideal Solution

With organizations wanting more clean and structured data that is easier to slice and dice for business intelligence and employees wishing for more conversational style mediums, an ideal solution would have to fill the gap of expectations.

In an ideal world, employees would be able to converse in a free-flowing manner using whatever means their group feels is best, while innovative technologies and products would analyze that content and extract valuable business information to form a structured database. Thankfully, traditional search technology along with linguistics and machine learning applied on very specific areas like sales and support can make this problem tractable.

We at Nimeyo are working on technology where employees can continue use E-mail, chat, SharePoint, Salesforce or anything else they desire, while our algorithms build the knowledge meta-layer on top of that.

With our “pods and bots” approach, our pods aggregate unstructured information and add a layer of intelligence, while our bots deliver answers right into your workflow. Essentially, we require no change in user behavior while still tapping into one of the richest sets of corporate information.
Drop us a note if you would like to learn more.

The Myth of One Golden Informational Warehouse

um

We in technology love the concept, the idealized form of a database.  A romantic notion, if you will, where there is one “base” of data with a formal and highly efficient way (like SQL) of pulling out data instantaneously.  And sure, if someone were to create a highly structured, highly coherent, from the ground up application where everyone is on the same page – a database is a great idea.  But the organization you work in is not a monolithic application, it’s a dynamic collection of people, ideas and information connected through shared goals.

The Uniqueness of Individuals – A Blessing and a Curse

Uniqueness causes ideas to flow and gives you a competitive edge, but uniqueness is the reason it is practically impossible to build and maintain a golden repository of information. Individuals and groups make their own choices based on their function and needs and enterprises are increasingly allowing these loosely-controlled but responsive environments to foster.

This is not to say that there is never a need for a structured repository of information. There is indeed a multi-billion dollar market of content management addressing various segments and use-cases including customer support portal, document management, e-discovery and others. However, attempts to manage and disseminate internal information, be it through documents, wikis, or social tools, even with the best intentions, have failed to address some of the fundamental needs for various reasons including:

  • Burden to systematically organize and manage content,
  • Poor findability of information,
  • Inability to personalize the information to suit individual role and needs, and
  • Disruptiveness in existing workflows and behaviors

As a result, although myriad of newer cloud services, each solving a narrow problem really well through superior usability, have gained significant traction and user base, information retrieval problem has continued to plague organizations.

IT versus the Worker

Companies more often than not play the symphony of implementing a new knowledge management structure followed by employees finding ways around it.  Let’s face it, when individuals or small teams have found out their own solution that works best for them, and a centralized authority claiming they are “wrong” or “inefficient” offers another solution, an inevitable backlash ensues.  One such consequence is Shadow IT, team specific systems without IT’s approval.

What happens when employees are told to move to a new system to store their information?  Take for example wiki pages.  Wikis, while important, were the hottest rage in 2012.

But unfortunately, it is plagued with inconsistent expectations and usage:

  • Many dutifully convert their knowledge into wiki pages.
  • Others view it as a burden and begrudgingly accept it, but put in little effort.
  • Some sparsely populate those pages
  • Some view them as brain dumps and treated it accordingly
  • Some resist and keep their information hidden within e-mail threads or other places within their own personal workflow.

All in all, motivation to centralize all knowledge does the opposite – it spreads it out even further.

Informational Drift

And when users have to alter their workflow to modify information, information does not get modified.  Plain and simple.  Wiki pages are often old and outdated.  Corrections are placed in e-mails, Slack messages, release notes – you name it, there’s information stuck in there.  Soon, getting the answer to a question becomes an exercise of informational connect the dots of various knowledge management solutions.  A customer’s information could be in Salesforce – their product spec could be in a Box document, internal product specs could be in a Confluence page, and the workaround needed to fix the particular bug the customer is encountering could reside in an e-mail thread.  No one planned it that way, but years of institutional and tribal knowledge from various individuals never ends up, figuratively and literally, on the same page

Taming the Beast

Leading experts agree:

  • According to McKinsey, employees spend 20% of their time looking for information.
  • IDC paints an even grimmer picture, stating that knowledge workers spend 30% of their workday searching for information.

IT organizations have struggled to find a solution to such a spaghetti network of informational resources.  Any solution not only needs to deal with their own KM deployments, but integrate with Shadow IT.  And these new breeds of solutions not only need to account for such fragmentation, but provide uniform and consistent results.

We at Nimeyo have personally struggled with this jigsaw approach of getting that right piece of information in a timely manner throughout our careers in companies both large and small.  That’s why we created a product that is built from the perspective of Enterprise employees. We call our architecture “Pods and Bots”. Pods are the domain specific (e.g. sales or support) engines that continuously analyze information from e-mail, documents, IM channels, wikis, CRM and more, and then automatically add a layer of domain specific intelligence.  And our bots then deliver this information to you, contextually and within your workflow, be it in e-mail or Salesforce; providing you the freedom to use your favorite tools without changing your behavior.

Can Enterprise Search effectively serve employees’ needs?

My toddler has an impressive collection of toys. He tries to keep his favorite ones somewhere “safe” but then he cannot find them when he wants to actually play with them. While trying to find it, we both know that the one that we are looking for is somewhere in the house and yet it remains alluringly out-of-reach since the exact places we determine to look for never have it. It’s frustrating for him to have this happen on regular basis.

Unfortunately, millions of enterprise employees feel similar frustration when they can’t find the information that they know is around them in various forms. They could be in mailing lists or in SharePoint or Box repositories, or in internal chat rooms, or on some internal wiki pages.

Realizing the potential value of unearthing information that employees need to do their jobs, companies – particularly large ones – employ enterprise search. Unfortunately, most of those engines remain poorly deployed and minimally used by the end users.

imageSo the question is why do enterprise search engines do such a poor job at engaging users whereas search outside of the corporate firewalls are part of our daily lives?
Although there are number of technical reasons, we believe the key problem is a “lack of user-orientation”. In other words, these solutions are neither attuned to the actual needs of the end users nor they understand the data itself in a meaningful way to be able to serve it in a meaningful way.

Let’s take few examples –

  1. Content and Users: Search engines’ key strength is in indexing wide ranging data types – web pages, documents, CRM systems etc. So when user searches with few keywords, search engines define success by uncovering wide-ranging data in a sequential manner based on some ranking criteria.However, not all data types are created equal. For instance, email communications are a lot more meta-data rich and time-relevant compared to documents. If used intelligently, such data specific analysis can be immensely useful in understanding how row communication relates to the end user needs.
  1. User interface:   Needless to say, we all are used to typing a simple search query (or question where there is a unique answer – e.g. “Father’s day 2015”) and expecting search engine to return “satisfactory” results. However, in a corporate context, this model is highly limiting as any one article or document is unlikely to provide a comprehensive answer in most real world scenarios.For example, when a sales rep gets into a competitive situation, a query like “MyCompany vs MyCompetitor” should surface variety of information including product differentiation, pricing, and nuggets from other similar situations. All these pieces are equally important to put together a competitive tactic to win the opportunity. A linear listing of results based on uniform ranking criteria does not do justice to needs of that sales rep.A UI that allows users to navigate through these various pieces in a consistent manner and “assist” in creating a cohesive picture would be much more effective in creating engaging user experience.And finally,
  1. User preferences and behaviors: In most of our enterprise search experience, presentation of results is “black magic”. Let’s say you searched for some information today, worked through the hits and were fortunate to find what you were looking for at the 20th You had to put the effort but you found what you were looking for!Unfortunately, say a month from now, if you are looking for the similar information through a similar query, you would still find the information deep down in the ranking.If solutions allowed ways to capture user preferences – expressed implicitly and explicitly – it would be able to return results that are a lot more aligned with what the end user needs are.

We at Nimeyo believe that to achieve the true potential of enterprise search, we need to stop viewing it through the prism of consumer search technologies.

To fulfill the promise, enterprise search products must understand not just who the user is and what role she performs in the organization but also identify optimal mechanism through which to deliver knowledge to the employee.